{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1381063-9676-4ee3-a420-e389076ab7aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from uniphm.data import Dataset\n",
    "from uniphm.data.labeler.BearingFaultLabeler import BearingFaultLabeler\n",
    "from uniphm.data.loader.XJTULoader import XJTULoader, Fault\n",
    "from uniphm.data.process.EntityPipeline import EntityPipeline\n",
    "from uniphm.data.process.array.RMSProcessor import RMSProcessor\n",
    "from uniphm.engine.Evaluator import Evaluator\n",
    "from uniphm.engine.metric.Accuracy import Accuracy\n",
    "from uniphm.engine.metric.WeightedF1Score import WeightedF1Score\n",
    "from uniphm.data.process.entity.ThreeSigmaFPTCalculator import ThreeSigmaFPTCalculator\n",
    "from uniphm.engine.tester.BaseTester import BaseTester\n",
    "from uniphm.engine.trainer.BaseTrainer import BaseTrainer\n",
    "from uniphm.engine.callback.CheckGradientsCallback import CheckGradientsCallback\n",
    "from uniphm.engine.callback.EarlyStoppingCallback import EarlyStoppingCallback\n",
    "from uniphm.engine.callback.TensorBoardCallback import TensorBoardCallback\n",
    "from uniphm.model.basic.CNN import CNN\n",
    "from uniphm.util.Cache import Cache\n",
    "from uniphm.util.Plotter import Plotter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd21ebf7-66d6-4983-9c04-1a614730b679",
   "metadata": {},
   "outputs": [],
   "source": [
    "cache_dataset = True\n",
    "# cache_dataset = False\n",
    "# cache_model = True\n",
    "cache_model = False\n",
    "\n",
    "fault_types = [Fault.NC, Fault.OF, Fault.IF, Fault.CF]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9853848e-5c6b-48db-95c1-f981b544e60b",
   "metadata": {},
   "source": [
    "# 获取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "714eb4d2-7150-4d65-bcb4-2ca4576a089d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[DEBUG   17:25:44]  [Cache]  -> Loading cache file: .\\cache\\diagnosis_dataset.pkl\n",
      "[DEBUG   17:25:44]  [Cache]  ✓ Successfully loaded: .\\cache\\diagnosis_dataset.pkl\n"
     ]
    }
   ],
   "source": [
    "dataset = Cache.load('diagnosis_dataset', is_able=cache_dataset)\n",
    "if dataset is None:\n",
    "    data_loader = XJTULoader('D:\\\\data\\\\dataset\\\\XJTU-SY_Bearing_Datasets')\n",
    "    labeler = BearingFaultLabeler(2048, fault_types, is_multi_hot=False, is_from_fpt=False)\n",
    "    pipeline = EntityPipeline()\n",
    "    pipeline.register(\n",
    "        processor=RMSProcessor(data_loader['continuum']),\n",
    "        input_key='Horizontal Vibration',\n",
    "        output_key='RMS'\n",
    "    )\n",
    "    pipeline.register(\n",
    "        processor=ThreeSigmaFPTCalculator(),\n",
    "        input_key='RMS',\n",
    "    )\n",
    "\n",
    "    # 通过其他轴承增加训练数据\n",
    "    dataset = Dataset()\n",
    "    for bearing_name in ['Bearing1_1', 'Bearing1_2', \"Bearing1_3\",\n",
    "                         'Bearing2_3', 'Bearing2_2', 'Bearing2_4', 'Bearing2_5',\n",
    "                         'Bearing3_3']:\n",
    "        bearing = data_loader(bearing_name, include='Horizontal Vibration')\n",
    "        pipeline.execute(bearing)\n",
    "        dataset.append_entity(labeler(bearing, 'Horizontal Vibration'))\n",
    "    Cache.save(dataset, 'diagnosis_dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a656bbfd-e40c-4a83-a8a5-b140a4bd8be1",
   "metadata": {},
   "source": [
    "# 划分数据集：训练集（70%）、验证集（15%）、测试集（15%）\n",
    "- 训练集：用于模型参数学习\n",
    "- 验证集：用于模型调参、早停（early stopping）、选择最佳模型\n",
    "- 测试集：只用于最终性能评估，不参与任何训练和调参过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f43ad36-8b09-413e-a965-37222f80ea49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:25:44]  [Dataset]  Splitting data by ratio 0.7 using random seed: 189104666664043153727204700797448531093\n",
      "[INFO    17:25:46]  [Dataset]  Splitting data by ratio 0.5 using random seed: 235068290572820748982181007615771808556\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = dataset.split_by_ratio(0.7)\n",
    "val_set, test_set = test_set.split_by_ratio(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "823725fc-5081-4805-9b0f-cee9cc3bbf89",
   "metadata": {},
   "source": [
    "# 配置测试算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "293c252e-e001-4edb-94c9-2cab03d35618",
   "metadata": {},
   "outputs": [],
   "source": [
    "tester = BaseTester()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59fd7912-af82-4648-b831-abef7330316d",
   "metadata": {},
   "source": [
    "# 配置训练算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26f9d1a2-173a-42f0-83d6-45e29dc51a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_config = {\n",
    "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
    "    'dtype': torch.float32,\n",
    "    'epochs': 10,\n",
    "    'batch_size': 128,\n",
    "    'lr': 0.001,\n",
    "    'weight_decay': 0.0,\n",
    "    'criterion': nn.CrossEntropyLoss(),\n",
    "    'callbacks': [\n",
    "        EarlyStoppingCallback(patience=20,\n",
    "                              val_set=test_set,\n",
    "                              metric=Accuracy(),\n",
    "                              tester=tester),\n",
    "        TensorBoardCallback(),\n",
    "        CheckGradientsCallback()\n",
    "    ]\n",
    "}\n",
    "trainer = BaseTrainer(config=train_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7df376-9292-4bc2-95f1-685aa2cd3b94",
   "metadata": {},
   "source": [
    "# 定义模型并训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9232eca8-fac5-49cf-a2a3-ac515c766f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:25:47]  \n",
      "[Trainer]  Start training by BaseTrainer:\n",
      "\ttraining set: Bearing1_1; Bearing1_2; Bearing1_3;…\n",
      "\tdevice: cuda\n",
      "\tdtype: torch.float32\n",
      "\tepochs: 10\n",
      "\tbatch_size: 128\n",
      "\tlr: 0.001\n",
      "\tweight_decay: 0.0\n",
      "\tcriterion: CrossEntropyLoss()\n",
      "\tcallbacks: [EarlyStoppingCallback, TensorBoardCallback, CheckGradientsCallback]\n",
      "\toptimizer: Adam\n",
      "[DEBUG   17:25:47]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.1299\n",
      "[INFO    17:25:49]  [BaseTrainer]  Epoch [1/10], CrossEntropyLoss:0.4433\n",
      "[DEBUG   17:25:49]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9363\n",
      "[WARNING 17:25:49]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:49]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:49]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[INFO    17:25:51]  [BaseTrainer]  Epoch [2/10], CrossEntropyLoss:0.1699\n",
      "[DEBUG   17:25:51]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9548\n",
      "[WARNING 17:25:51]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:51]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:51]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[INFO    17:25:52]  [BaseTrainer]  Epoch [3/10], CrossEntropyLoss:0.1160\n",
      "[DEBUG   17:25:52]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9687\n",
      "[WARNING 17:25:53]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:53]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:53]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[INFO    17:25:54]  [BaseTrainer]  Epoch [4/10], CrossEntropyLoss:0.0860\n",
      "[DEBUG   17:25:54]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9744\n",
      "[WARNING 17:25:54]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:54]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:54]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[INFO    17:25:56]  [BaseTrainer]  Epoch [5/10], CrossEntropyLoss:0.0643\n",
      "[DEBUG   17:25:56]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9832\n",
      "[WARNING 17:25:56]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:56]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:56]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[INFO    17:25:58]  [BaseTrainer]  Epoch [6/10], CrossEntropyLoss:0.0569\n",
      "[DEBUG   17:25:58]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9751\n",
      "[DEBUG   17:25:58]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[WARNING 17:25:58]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:58]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:58]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:25:58]  [CheckGradients] se2.excitation.2.weight gradient is very small: 3.30e-06\n",
      "[WARNING 17:25:58]  [CheckGradients] fc1.weight gradient is very small: 4.90e-06\n",
      "[INFO    17:25:59]  [BaseTrainer]  Epoch [7/10], CrossEntropyLoss:0.0500\n",
      "[DEBUG   17:25:59]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9795\n",
      "[DEBUG   17:25:59]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[WARNING 17:26:00]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:00]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:00]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:00]  [CheckGradients] se2.excitation.2.weight gradient is very small: 4.47e-06\n",
      "[INFO    17:26:01]  [BaseTrainer]  Epoch [8/10], CrossEntropyLoss:0.0478\n",
      "[DEBUG   17:26:01]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9808\n",
      "[DEBUG   17:26:01]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[WARNING 17:26:02]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:02]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:02]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:02]  [CheckGradients] fc1.weight gradient is very small: 5.92e-06\n",
      "[INFO    17:26:03]  [BaseTrainer]  Epoch [9/10], CrossEntropyLoss:0.0378\n",
      "[DEBUG   17:26:03]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9837\n",
      "[WARNING 17:26:04]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:04]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:04]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:04]  [CheckGradients] se2.excitation.2.weight gradient is very small: 9.80e-06\n",
      "[WARNING 17:26:04]  [CheckGradients] fc1.weight gradient is very small: 5.96e-06\n",
      "[INFO    17:26:05]  [BaseTrainer]  Epoch [10/10], CrossEntropyLoss:0.0397\n",
      "[DEBUG   17:26:05]  [EarlyStopping]  On the validation set Bearing1_1; Bearing1_2; Bearing1_3; Bearing2_3; Bearing2_2; Bearing2_4; Bearing2_5; Bearing3_3, the Accuracy is 0.9874\n",
      "[WARNING 17:26:05]  [CheckGradients] se1.excitation.0.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:05]  [CheckGradients] se1.excitation.0.bias gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:05]  [CheckGradients] se1.excitation.2.weight gradient is very small: 0.00e+00\n",
      "[WARNING 17:26:05]  [CheckGradients] se2.excitation.2.weight gradient is very small: 6.69e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[DEBUG   17:26:06]  [Cache]  Generating cache file: .\\cache\\diagnosis_model.pkl\n",
      "[DEBUG   17:26:06]  [Cache]  Generated cache file: .\\cache\\diagnosis_model.pkl\n"
     ]
    }
   ],
   "source": [
    "model = Cache.load('diagnosis_model', cache_model)\n",
    "if model is None:\n",
    "    model = CNN(2048, len(fault_types))\n",
    "    # 配置训练算法\n",
    "    # 开始训练\n",
    "    losses = trainer.train(model=model, train_set=train_set)\n",
    "    Plotter.loss(losses)\n",
    "    Cache.save(model, 'diagnosis_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be48c93-0bfe-41da-8524-a73d00b9ca6a",
   "metadata": {},
   "source": [
    "# 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "977ef312-d352-4cdd-b44e-c4111b50af65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = tester.test(model, test_set)\n",
    "Plotter.confusion_matrix(test_set, result, types=fault_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "759c4e44-ecf9-4466-8726-0743f48174e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plotter.fault_evolution(test_set.get('Bearing1_1'), result.get('Bearing1_1'), types=fault_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "561d1566-dafe-4f96-a92f-3f3f0ce344fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GDx9edL1MJhP9+/ePsWPHxvvf//7YddddK/5TcQAAAEB78jkAAADUl2wOAAAA3a9bJsRfeOGF7f7/4osvjoiI0047Lbbeeuvu2CUAAABQQD4HAACA+pLNAQAAoPt1y4T4QieddFJERIwZM6YWuwMAAABSyOcAAABQX7I5AAAAVF9NJ8QDAFB766+/fjz66KMdngOg95HPAegMWQIAoPpkcwBofsZMAKDx1GRCPAAA9dPa2hrvfOc7610MAACgycgSAAAAAB0ZMwGAxtNSi53MmDEj+vTpEwMGDIiXXnqp7PIvvfRS9O/fP1pbW+OBBx6oQQkBAACg55PPAQAAoL5kcwAAAKi+mkyIv+yyyyJJkth3331j3LhxZZcfN25c7LfffrFmzZq49NJLa1BCAAAA6PnkcwAAAKgv2RwAAACqryYT4u+8887IZDKx1157VbzOPvvsExERt99+e3cVCwAAAHoV+RwAAADqSzYHAACA6qvJhPhnn302IiK23nrritfZcsstIyLimWee6ZYyAQAAQG8jnwMAAEB9yeYAAABQfa212Mny5csjIqJ///4Vr9OvX7+IiFi6dGm3lAkAoLdYtWpVPPnkk+2e22KLLaK1tSZdQQAaiHwOQGfIEgAA1SebA0DzM2YCAI2nJr8QP2LEiIiI+Pe//13xOrNnz46IiOHDh3dHkYpatGhR/OxnP4u99947Ntlkkxg4cGAMGzYsJk+eHJ/+9KfjD3/4Q6xevbrkNk4++eTIZDIV/5s1a1ZtDg4A6JVee+212Gabbdr9e+211+pdLADqQD6XzwE6Q5YAAKg+2Vw2B6D5GTMBgMZTkwnx2T/3ds0111S8zlVXXRURbd+eq5Xzzz8/Nt988/jyl78cN9xwQ7zwwgvx5ptvxhtvvBFPP/10XHrppfHJT34ytt1227jzzjtrVi4AAACoBvkcAAAA6ks2BwAAgOqryd9p2XvvvWP69Onx29/+Ng477LDYeeedSy5/++23xyWXXBKZTCb23XffWhQxvvGNb8SPfvSjiIhobW2NT37yk7H//vvHxIkTY+XKlfHkk0/GpZdeGrfccks89thjsdtuu8Xvfve7+NjHPlZyu7/5zW9i6tSpJZcZN25c1Y4DAAAAipHP5XMAAADqSzaXzQEAAKi+mkyI/+IXvxhnnXVWvPrqq7H33nvHGWecEUceeWT079+/3XLLly+PX/3qV/Hd7343Vq1aFSNGjIijjz6628v3f//3f7lAP378+Lj22mvj3e9+d7tldtppp/j85z8fl156aRxxxBGxYsWK+MxnPhOTJk3qsGy+TTfdNLbZZptuLD0AAABURj6XzwEAAKgv2Vw2BwAAoPpqMiF+8ODBcemll8bee+8dy5Yti+OOOy6+853vxPbbbx9jx46NiIhXXnklZs6cGcuWLYskSaK1tTV+//vfx9ChQ7u1bC+88EJ8/etfj4iIQYMGxc033xyTJ08uuvx//ud/RpIk8ZnPfCZWrFgRn/3sZ+Of//xnZDKZbi0nAAAArCv5HAAAAOpLNgcAAIDqa6nVjnbbbbe48cYbY+zYsZEkSSxZsiRuv/32+MMf/hB/+MMf4vbbb4+lS5dGkiQxbty4+Nvf/ha77757t5frnHPOieXLl0dExIknnlgy0Gd9+tOfjo985CMREfHoo4/Gdddd161lBAAAgGqRzwEAAKC+ZHMAAACorppNiI+I+NCHPhTPPvts/PKXv4z99tsvxo0bF/369Yt+/frFuHHjYv/994/zzz8/nnnmmZg2bVq3lydJkvjtb38bEREDBgyIL37xixWve/zxx+ceX3jhhdUuGgAAAHQb+RwAAADqSzYHAACA6mmt9Q779esXRx11VBx11FG13nUH//rXv2LhwoUREbHzzjvHsGHDKl53t912iwEDBsSbb74Zd955Z3cVEaC9xXMjFjwZMWqLiCEb1Ls05TVCeRuhDNDIyl0j2dcHjIh4c+Ha5RbPjfj3PRGZiJiwY9uyacsV7iO7XOHrL94T8ebrEQOGl99eV4+l0uPK38acRyOeuzVis2kRG25TfH+ljiuJiJGbR7z6bNv56j88Ys4jpbc5YETEwmfb1t14x/bHkz33ywvOV3ZfA/K2P2j02uc3zjuv+cf71F8j3ng5Yqv92sqTVoaRm689T/nbKFVPheey2DlPO/bsuZqQcuz520iSiBdmRAzdKGLc9mu3u3R+xOPXrl0ve2z528hfd/JHOtf+K1mu1LlJk3a+stfYiM07tofCfXamDkqd+1J1U7jt/PeAUvvMvw6yZX312bY2HBGx/I2It5ZEbLlfx2siTaXXpc/9suRzgAZSyWdYVz7nSvVZi73W7nHK76ncdU7EtntEZDKV9RsKX8vvy815JGLDKW3bKtWfSZKIp/8W8dayiLHvXtv3K1znX1dFPHxZxBYfiRiwflvGiGjrg2f7w+X6MIXnItv3jmjra4/YvH2/87lb1x5DuX5X/rl46q8Ri1+O2Pj9a9eNKN9/z2an/GOqpF2k9d+K9fMq7dvmZ5PC85zWt087ns5muGy7yead7Hnccr+2/68kzxXbVqm+cvY85O8vIuKJayOGbBTRf1jba8MnRmyw9dp2UirbRXQsY+H+8ttDWtbMf37Oo2vLs/4mbXknSSLeWhoxanLbdVNJrnjpgbUZMWJt+bKP09p7YbvNbxfL34hYuSRi4vvb2m7+9ZTNx2nvA9nznZ9X8+slPy/llzmtLtPy5oiCnFvs2su/zrPlL3wvSHu/yy9fWp4uly1L1VX+8WbPS7b+1xscsf7GHd/jCsddCrdZagziqb9GLHgqYsiGEf2Grt1G4bkpluNLva8V7ie/HtOyZLnMWliX2eWeuy3isasjNv1gxMAR6e8TER3fM/LLNfH9bfVfqkzF3nfy66f/0Lbnsu+ZaeulHVP2M235GxFL5kQM3rDt/5fMaaub4Sn1nraNtDZQrG+Qv86IlGPOLvPavzvmevm8YrI5ADS4Yhms1PLF7vek9cUr6U+m9avT8lK5HFwsvxQeX/7y2X5/sb5q2r2qbMbI9mGz+g9vf88xP8+m9cXT7uel7fPVZyNe/3db9iy8J1g4jrJ0/tq++fCN15Ynf4yg8DjL9dlL1XNaP7zwvGfvI7/+77b+/ajJa3N937wMkc0C6w1qy/rrDWrL0oUZqtLxh2x2X/FGxOK3M0Z2X1nZLLDw7fKtWLJ2f4UZrvD+dX4Z0vaff/zZnDvx/WvHB9Lqtdy1UzhmUdjWS93XTrsOyl37cx5tuyfdb3B6JiscV6xkHkThsRReX/ltNaL0cWcz7csPRfQdFPGOPdLHIQvnAqSNW1Y6BlzuGF56IOLlf0T0HdhWnux4wojN08dsir1n5p/H7H3n/JxdWM7C815s3LLU2HX++1r+OGh23CR7DaWN22bPd7Fx3WKPi92Dz6+L/HZa6j27l2Tzmk+IbyQPP/xw7vF73vOeTq3bp0+feNe73hX33HNPzJ8/P15++eXYaKONOiz3/PPPx6hRo4puZ9NNN41BgwZ1at+zZ88u+forr7zSqe0BTeKBiyP+8l8Ra1ZFtLRG7PPjiO0Pq3epimuE8jZCGaCRlbtG8l/PammN2OZjEY9cHpGsefvJTFsIyP1/rN1exNptZN6eTJOsaf/6dce3X7fU9opdw5253ksd16N/XLuNsdtFvHT/2mW2/WTEwb/suI2KjytFsW3my7RE7HtO2/E8cHH6+WorQOl95Z/X7PH+8w9r17vtzIhxUyNeeahjGfLLkj3W/MdZafVeeG4Ll80eVyXHnrZM2nEWnovbzmw71xPfX2QbmYj9flJZ+6/kOil1btLaZWH77XCN5Re1JWLKIR3PZ+F+IorXQblzX+z1Yu07W09p+yx2HGluPbP9NZHmz1+M+Odla/+/2DXkc7/pNGM+l82BqqnkM6wrn3Ol+qz5fYNifbyW1ogJ+3Tc7l3nRvzjp22Py/UbsmWsuC8X6f2ZStbpNyzizVfb/v+pG1IWKug3p/VhCs9FkkTxfnZKv7NUvyvrgYsjrj2u47od9pfSf+/QtypYplR/s7D/lt+nLHXOi/Vti/b1UvJcseMpdZ4Ky1+uLdx6ZuTqpFSeK7qtvHXT2nSmpf0x3Xpm8bIUk59vIjr2bfPzWGF7yG+vaX3xF2a031YlZSmVKyLaclQpxcYHIqL4ddOJ8uUf/21ntq/DYnn7toJ2UMl7SalrL+06L5T2fpdWvrSxg3LZMrtesbrKnpeIjvVfrM2U22ZhDk97vyql5BhGkfeswv1U0vaKXqcp182Dl6x9P5j567XbKHyfyJcdQ6j0+Eu97xRe6/kyLREbbd9+vVLvBeUU+2wr3Eba50Bh36DcftOWuTVl/EU+byrNmM0j5HMAulFans7PdYWu+3rEi38pfp+kWF+8XI4t7Ffn98GK5YK0HFwuv3QoX8G+CvuqEZXfAyqULUthni15Py865vNC+fcEK7lXmyb/ONPqpvD1YvVcOG5SKqeti8IMVcn4w8sPdH3/hftLy3D5ZajG/ovdxy6W7SoZmyx137Ow/tKu/bSsV7aOy8yDKJUfK1Eqa0dE3P3TteVMy3zFrq9Kx4AjOncM2fKUOp60eQal3n/KjfmmKjaeWU6pcaO8baae74LXs+UrNvck7ZyX+3wofK/qJdk8kyTJOo5MNq+f/OQnuT/fds4558Rxxx3XqfUPPPDAuPrqqyMi4p///GdMmTIlIiJOPvnkOOWUUyraxvTp0zv9J+4ymUz5hd724osvxvjx4zu1faABLZ4bcfbWHTtnX3usMb/B1QjlbYQyQIOYP39+jBkzpt1z8557NEZf8sHi10jaNdRZLa1tHftkdfrrmT5t/y32etr20q7hzlzv63pcX7qr7RvupbbR0hqxZk1EVBiYKt3mF26L+NUu61YntZDp0xbeKp24VO64Mn0ivnh7dY4906dEe2uJ+PoT5dv/ulwnae2yGtdaoUrqoNy5L/Z6pk9bll5TeB5bIlpaqnMcX7or/Zfi5zwa8YsPpC+fdg012Of+7NmzY8KECREhp6VpxnwumwNVUUlftiv5tkp9jPlL18SY/13S7rl53xgcowe1tC9LWr8hW8aIzpelXJaomir2YYruoqD/+OOtuu+41qW/2Zl+fHforgyX79BrIn53cPltdWf+yh7n0vnpfduS6/aJWJNEh6xZbjJAT1JpHWbVql135f2u2mMwaTJ93r7h2sn2kb0GfvnBrr1fZXN8ufVz19oHUzJmhWUse522RKePv5o+fnHEFbW+2VyDz7ayRejTvk4bKJ/L5qU1YzaPkM8B6CbFMvzbfZv5CxbEmM3a38voMGayrg69JuKSg7o+jpCWofLzS3eOUVSkSH+9sD/ZFQ2blVvengTbzed9XcYfqiXTJ+KzV0b8dv/67L+SrN6Z+/r5uabYPcu2BSNaMp1rwy2tEZ/5c3XO1bpk7Yq2W2QMuOx5qaGWPm3zwuv6/lYlaWNbXR23aaBsHtF9+byKn8LNZ/HixbnHgwcP7vT6+eu88cYbVSkTQKoFT3bsnK1Z1fZ8I2qE8jZCGaCRvfps6Wsk7RrqrDWrSnfAk9Wd66AXu4Y7c72v63E9d2v5baxZFZ262VrpNp+7tfEnw0e01Wml5azkuJLV1Tv2ku1tTWXtf12uk7R2WY1rrVAldVDu3Bd7PVldZPBkTfWO47lbO/+8z/2mJ58DvVYln2Fd+Zzrjj5GMcX6DdkydqUs5bJE1VSxD1N0FwX9x+48rnXpb3amH98duivD5Xvs6sq21Z35K3ucxfq2JdddHalZsyFv8HeTSuswq1btuivvd9Ueg0mTFGkz5WSvga6+X2VzfLn1c9daF/ZT8XVa5+vj4XX4Zb8uq8FnW9kiFNSpfN40ZHMAyFMsw2f7Nq8+2/1leOzqdRtHSMtQ+fml7pNFi/TXqzGJt2Gz8pranPd1GX+olmR1Wxusl0qyemfu6+fnmpLndU3n2/CaVdU7V+uStSvabolx6nq2t3xrOjkPpZGljW11ddyml2Tz1mpubLPNNouItm9hP/vssx2e74rCbVXTkCFDco+XLFlSYsl0+esMHTo0dZmufIu9nBdffLHk66+88kq8973vreo+gTobtcXb30ws+MbXqC3qV6ZSGqG8jVAGaGQjNy99jaRdQ53VHb8Qn3YNd+Z6X9fj2mxa2y9Rl9pGZ38hvtJtbjZt3eukFjr7C/HljivTp3rHXu4X4itp/+tynaS1y2pca4Uq/YX4Uue12Ou1+IX4zaZ1/vm0a6iXf+7L5x1VO5/L5kBVVNKX7Uq+7Y4+RjHF+g35ZexsWXraL8Tn9x9L9kmruK+sSttCI/xCfHdkuHxbHxDx4MXlt9Wd+St7nANGdGFdvxBfcR1m1fIX4jv7flftMZg06/IL8ZtN6/r7VTbHl1s/d6114dcfK75O6/wL8e/6ZMRTN9R4pw36C/G9NJ/L5h25dw5A0yiW4XN9mwXdX4atD4h44KKujyOkZaj8vll3jlFUxC/Ed99u1mH8oVoyfdra4Mxf12f/lWT1zv5CfHZ7xe5Zti3YtV+Ir9a5WpesXdF2S4wBlzwvNdQbfiG+K+M2vSSbV/UX4mfNmpX7V+z5rvzrLqNGjco9njNnTqfXnzt3bu7xyJEjq1KmSowfP77kv7Fjx9asLECNDNkgYp8ft304RbT9d58fN8yfMemgEcrbCGWARjZ4TOlrpPAaymppjdj2k22DCDmZgv/P296+Z6/dRqZl7XItrW2v7Xt2x3VLbS/tGu7M9V7uuPK3MW5q+2W2/WTEhtt03Ebhce3z44j9zkk5rhTFtpkv09L22obbtP037XxFJX8WONO+nNt+suN646amlyG/LNlt5D/OytZrYX3kn9v8ZfOPq9ix73t26WXSjjPNtp9s3x4L19nvnMrafyXXSbFzk9Yu09pvh2usYNtp5zN/P+XqoNy5L/Z6btsF122mpe38deY4isleE2k23ObtdpuyvM/9DuTz7iebA1VRyWdYVz7nyvVZ8/sGxfp4La0RWx9Uuvyl+g3ZMpbq65baZtG+W5F1BpR7/y/oN6f1YQrPRcl+dsprxfpd+f3Hfc9OX7fD/lL676WyU7n+ZmH/rbCchX3I/GXT+rZF+3opea7Y8axLhkuVWbu9tDy32S4ltpW3blqb7my/NrV4LWuPM61vm5/HCttDpqWtH16YNTMtEfue03FblZSlVK6oRLHxgYryaRlp119+HZasj7y6LNauS+4rvw1XcCxp73dp5UsbOyiXLbPrlaqrbT+ZXv/ZPJ02PlFqm/ljEMXer0rJz/EdxnxS3rM23KatbXdmPyWv05T3+cL3g+w20p7P2vaTEfudW3m5ir3vvPPA0tdnpqXjeqXeC8op9tlWuI20z4HCvkG5/RZbZttPttWpfB4RsnmtyOcAdItshi/MYNm+zeAxHdfZ+qDS90mK9cVL5di0fnWhtFyQloML80va8bUrX4n7iGnjBZ3R0trWd00r9z4lxoQqvf+67zldL1/+cabVTeHrxeo5LYvtd06R++NVVMn4Q3fuP1uGfc9ua4PV2n/afexi2a6Sscli9/XT6i//nEakn9fscvud0/Fe6toFit9DTjtXnVVp1i6W54rVSyVjwBHFz0tXFZtnUOr9JzuGVmyeSvpKXbwmyowdlxxnLni92Ph82n3//HGbUp8Phe9VvSSbZ5IkSaq1sSOOOCL3+MILL0x9vivyt1VNjzzySGy77bYREbHnnnvGX//614rXXb16dQwdOjSWLVsWo0ePjnnz5uVeO/nkk+OUU06JiO75lns5s2fPjgkTJkRE2zfix48fX9P9A91o8dy2P18yaovm+JBqhPI2QhmgzubPnx9jxrQflJk3b16MHj26/DWSfX3AiIg3F65dbvHciH/f09annrBj27Jpy+VvI/tt08L9LZ4b8eI9EW++HjFgePntFdOZ673UceVvY86jbX9uarNpHSfpVnpcSbT9Iv+rz7adr/7DI+Y8UnqbA0ZELHy2bd2Nd2x/PNlzv7zgfGX3NSBv+4NGr31+47zzmn+8T/014o2XI7bar608aWUYufna85S/jVL1VHgui53ztGPPnqsJKceev40kiXhhRsTQjSLGbb92u0vnRzx+7dr1sseWv438dSd/pHPtv5LlSp2bNGnnK3uNjdi8Y3so3Gdn6qDUuS9VN4Xbzn8PKLXP/OsgW9ZXn21rwxERy9+IeGtJxJb7FZ8Mn6/S67LBPvdrndPk8zb1zOeyOdAplXyGdeVzrlSftdhreY/nL2/pmCWu/E6M3naPiEymsn5D4Wv5fbk5j0RsOKVtW6X6M0kS8fTfIt5aFjH23Wv7foXr/OuqiIcvi9jiIxED1m/LGBFtffBsf7hcH6bwXGT73hFtfe0Rm7fvdz5369pjKNfvyj8XT/01YvHLERu/f+26EeX779nslH9MlbSLtP5bsX5epX3b/GxSeJ7T+vZpx9PZDJdtN9m8kz2PW+7X9v+V5Lli2yrVV86eh/z9RUQ8cW3EkI0i+g9re234xIgNtl7bTkplu4iOZSzcX357SMua+c/PeXRtedbfpC3vJEnEW0sjRk1uu24qyRUvPbA2I0asLV/2cVp7L2y3+e1i+RsRK5dETHx/W9vNv56y+TjtfSB7vvPzan695Oel/DKn1WVa3hxRkHOLXXv513m2/IXvBWnvd/nlS8vT5bJlqbrKP97secnW/3qDI9bfuON7XOG4S+E2S41BPPXXiAVPRQzZMKLf0LXbKDw3xXJ8qfe1wv3k12NaliyXWQvrMrvcc7e1/fn5TT8YMXBE+vtERMf3jPxyTXx/W/2XKlOx9538+un/9q9mZ98z09ZLO6bsZ9ryNyKWzIkYvGHb/y+Z01Y3w1PqPW0baW2gWN8gf50RKcecXea1f3fM9Q2az2Xz0npiNo+QzwFYR0UyWNH7r/3XFL/fk9YXr6Q/mdavTstL5XJwsfxSeHz5y2f7/cX6qmn3qrIZI9uHzeo/vP09x/w8m9YXT7ufl7bPV5+NeP3fbdmz8J5g4TjK0vlr++bDN15bnvwxgsLjLNdnL1XPaf3wwvOevY/8+r/b+vejJq/N9X3zMkQ2C6w3qC3rrzeoLUsXZqhKxx+y2X3FGxGL384Y2X1lZbPAwrfLt2LJ2v0VZrjC+9f5ZUjbf/7xZ3PuxPevHR9Iq9dy107hmEVhWy91XzvtOih1TrPH9fi1Ef0Gp2eywnHFSuZBFB5L4fWV31YjSh93NtO+/FBE30ER79gjfRyycC5A2rhlpWPA5Y7hpQciXv5HRN+BbeXJjieM2Dx9zKbYe2b+eczed87P2YXlLDzvxcYtS41d57+v5Y+DZsdNstdQ2rht9nwXG9ct9rjYPfj8ushvp6Xesxsom0d0X06r6oT4ZpMkSYwaNSoWLlwYAwYMiFdeeSWGDRtW0bo33nhjfOQjbR+0Bx98cPzpT3/KvSbUAwCNpOSEeABqQk4rrSfmc3UO9ASyBADQk8hppfXEbB6h3gHoHsZMAKDruiundfPfv2hsmUwmDj300IiIePPNN+P888+veN3zzjsv9/jwww+vdtEAAKpm2LBhMX369Hb/Kr2RAQC1IJ8DNCZZAgCg95DNAaByxkwAoPG01rsA9XbcccfFz3/+81ixYkWccsopceCBB8akSZNKrnPZZZfFX/7yl4iI2GabbWLfffetRVEBALqkb9++Nf/VHQDoLPkcoPHIEgAAvYtsDgCVMWYCAI2nV/9CfETEJptsEj/84Q8jImLJkiXx4Q9/OB5++OGiy19++eVx2GGHRURb5+aSSy6JTCZTk7ICAABATyWfAwAAQH3J5gAAADSrqv5C/O23317NzeV88IMf7JbtZn3lK1+JZ599Nn7yk5/Ev//979hhhx3iU5/6VOy///4xceLEeOutt+KJJ56ISy+9NG6++eaIiOjXr1/87ne/i3e/+93dWjYAAADoLPkcAAAA6ks2BwAAgNqp6oT4adOmVf0b35lMJlatWlX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      "text/plain": [
       "<Figure size 3000x2100 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plotter.fault_evolution(test_set, result, types=fault_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967332e8-bd71-47d7-85a4-e0da9b2d4e50",
   "metadata": {},
   "source": [
    "# 预测结果评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a90870b1-ef55-42c7-8e4b-f81299415264",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:26:07]  \n",
      "[Evaluator]  Performance Evaluation:\n",
      "             Accuracy WeightedF1Score\n",
      "Bearing1_1     0.9595          0.9592\n",
      "Bearing1_2     0.9793          0.9791\n",
      "Bearing1_3     0.9921          0.9921\n",
      "Bearing2_3     0.9984          0.9984\n",
      "Bearing2_2     0.9871          0.9871\n",
      "Bearing2_4     0.9703          0.9701\n",
      "Bearing2_5     0.9840          0.9840\n",
      "Bearing3_3     0.9877          0.9928\n",
      "mean(global)   0.9874          0.9874\n",
      "mean(group)    0.9874          0.9884\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>WeightedF1Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bearing1_1</th>\n",
       "      <td>0.9595</td>\n",
       "      <td>0.9592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearing1_2</th>\n",
       "      <td>0.9793</td>\n",
       "      <td>0.9791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearing1_3</th>\n",
       "      <td>0.9921</td>\n",
       "      <td>0.9921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearing2_3</th>\n",
       "      <td>0.9984</td>\n",
       "      <td>0.9984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearing2_2</th>\n",
       "      <td>0.9871</td>\n",
       "      <td>0.9871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearing2_4</th>\n",
       "      <td>0.9703</td>\n",
       "      <td>0.9701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearing2_5</th>\n",
       "      <td>0.9840</td>\n",
       "      <td>0.9840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearing3_3</th>\n",
       "      <td>0.9877</td>\n",
       "      <td>0.9928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean(global)</th>\n",
       "      <td>0.9874</td>\n",
       "      <td>0.9874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean(group)</th>\n",
       "      <td>0.9874</td>\n",
       "      <td>0.9884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Accuracy WeightedF1Score\n",
       "Bearing1_1     0.9595          0.9592\n",
       "Bearing1_2     0.9793          0.9791\n",
       "Bearing1_3     0.9921          0.9921\n",
       "Bearing2_3     0.9984          0.9984\n",
       "Bearing2_2     0.9871          0.9871\n",
       "Bearing2_4     0.9703          0.9701\n",
       "Bearing2_5     0.9840          0.9840\n",
       "Bearing3_3     0.9877          0.9928\n",
       "mean(global)   0.9874          0.9874\n",
       "mean(group)    0.9874          0.9884"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator = Evaluator()\n",
    "evaluator.add(Accuracy(), WeightedF1Score())\n",
    "evaluator(test_set, result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
